mohammad reza soleymani - ASSistant professor

An adaptive inverse neuro-controller is proposed capable of acuurately determining the optimal cutting feed to achieve thedesired value of the cutting force under given perturbations then it is shown that an adaptive multi operation process optimizing control sceme based on a clustered neural network provides much better modeling performance and global control stability than one built with a perceptron network during multi -operation milling processes subjected to operating input and output constraints during side milling and slotting reductions of up to 32% of the mean incurred cost are accessible together with excellent surface finishes in the finish operations.

process modeling-Adaptive control-machining optimization-cutting force regulation-on-lineadaptation